{"id":"https://openalex.org/W3107969206","doi":"https://doi.org/10.1093/jamia/ocaa283","title":"Addressing bias in prediction models by improving subpopulation calibration","display_name":"Addressing bias in prediction models by improving subpopulation calibration","publication_year":2020,"publication_date":"2020-10-26","ids":{"openalex":"https://openalex.org/W3107969206","doi":"https://doi.org/10.1093/jamia/ocaa283","mag":"3107969206","pmid":"https://pubmed.ncbi.nlm.nih.gov/33236066"},"language":"en","primary_location":{"id":"doi:10.1093/jamia/ocaa283","is_oa":false,"landing_page_url":"https://doi.org/10.1093/jamia/ocaa283","pdf_url":null,"source":{"id":"https://openalex.org/S129839026","display_name":"Journal of the American Medical Informatics Association","issn_l":"1067-5027","issn":["1067-5027","1527-974X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of the American Medical Informatics Association","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7936516","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091779604","display_name":"Noam Barda","orcid":"https://orcid.org/0000-0002-3400-235X"},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]},{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]},{"id":"https://openalex.org/I64767286","display_name":"Clalit Health Services","ror":"https://ror.org/04zjvnp94","country_code":"IL","type":"healthcare","lineage":["https://openalex.org/I64767286"]}],"countries":["IL","US"],"is_corresponding":true,"raw_author_name":"Noam Barda","raw_affiliation_strings":["Clalit Research Institute, Clalit Health Services, Tel-Aviv, Israel","Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA","School of Public Health, Ben-Gurion University, Beer-Sheba, Israel"],"raw_orcid":"https://orcid.org/0000-0002-3400-235X","affiliations":[{"raw_affiliation_string":"Clalit Research Institute, Clalit Health Services, Tel-Aviv, Israel","institution_ids":["https://openalex.org/I64767286"]},{"raw_affiliation_string":"Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA","institution_ids":["https://openalex.org/I136199984"]},{"raw_affiliation_string":"School of Public Health, Ben-Gurion University, Beer-Sheba, Israel","institution_ids":["https://openalex.org/I124227911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037714222","display_name":"Gal Yona","orcid":null},"institutions":[{"id":"https://openalex.org/I53964585","display_name":"Weizmann Institute of Science","ror":"https://ror.org/0316ej306","country_code":"IL","type":"education","lineage":["https://openalex.org/I53964585"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Gal Yona","raw_affiliation_strings":["Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel","institution_ids":["https://openalex.org/I53964585"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057788395","display_name":"Guy N. Rothblum","orcid":"https://orcid.org/0000-0001-5273-6472"},"institutions":[{"id":"https://openalex.org/I53964585","display_name":"Weizmann Institute of Science","ror":"https://ror.org/0316ej306","country_code":"IL","type":"education","lineage":["https://openalex.org/I53964585"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Guy N Rothblum","raw_affiliation_strings":["Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel","institution_ids":["https://openalex.org/I53964585"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005650203","display_name":"Philip Greenland","orcid":"https://orcid.org/0000-0002-6327-2439"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philip Greenland","raw_affiliation_strings":["Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057087955","display_name":"Morton Leibowitz","orcid":null},"institutions":[{"id":"https://openalex.org/I64767286","display_name":"Clalit Health Services","ror":"https://ror.org/04zjvnp94","country_code":"IL","type":"healthcare","lineage":["https://openalex.org/I64767286"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Morton Leibowitz","raw_affiliation_strings":["Clalit Research Institute, Clalit Health Services, Tel-Aviv, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clalit Research Institute, Clalit Health Services, Tel-Aviv, Israel","institution_ids":["https://openalex.org/I64767286"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079572059","display_name":"Ran D. Balicer","orcid":"https://orcid.org/0000-0002-7783-6362"},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]},{"id":"https://openalex.org/I64767286","display_name":"Clalit Health Services","ror":"https://ror.org/04zjvnp94","country_code":"IL","type":"healthcare","lineage":["https://openalex.org/I64767286"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Ran Balicer","raw_affiliation_strings":["Clalit Research Institute, Clalit Health Services, Tel-Aviv, Israel","School of Public Health, Ben-Gurion University, Beer-Sheba, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clalit Research Institute, Clalit Health Services, Tel-Aviv, Israel","institution_ids":["https://openalex.org/I64767286"]},{"raw_affiliation_string":"School of Public Health, Ben-Gurion University, Beer-Sheba, Israel","institution_ids":["https://openalex.org/I124227911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083255118","display_name":"Eitan Bachmat","orcid":"https://orcid.org/0000-0001-7153-100X"},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Eitan Bachmat","raw_affiliation_strings":["Department of Computer Science, Ben-Gurion University, Beer-Sheba, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Ben-Gurion University, Beer-Sheba, Israel","institution_ids":["https://openalex.org/I124227911"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057364990","display_name":"Noa Dagan","orcid":"https://orcid.org/0000-0001-8811-7825"},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]},{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]},{"id":"https://openalex.org/I64767286","display_name":"Clalit Health Services","ror":"https://ror.org/04zjvnp94","country_code":"IL","type":"healthcare","lineage":["https://openalex.org/I64767286"]}],"countries":["IL","US"],"is_corresponding":false,"raw_author_name":"Noa Dagan","raw_affiliation_strings":["Clalit Research Institute, Clalit Health Services, Tel-Aviv, Israel","Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA","Department of Computer Science, Ben-Gurion University, Beer-Sheba, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clalit Research Institute, Clalit Health Services, Tel-Aviv, Israel","institution_ids":["https://openalex.org/I64767286"]},{"raw_affiliation_string":"Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA","institution_ids":["https://openalex.org/I136199984"]},{"raw_affiliation_string":"Department of Computer Science, Ben-Gurion University, Beer-Sheba, Israel","institution_ids":["https://openalex.org/I124227911"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5091779604"],"corresponding_institution_ids":["https://openalex.org/I124227911","https://openalex.org/I136199984","https://openalex.org/I64767286"],"apc_list":{"value":3967,"currency":"USD","value_usd":3967},"apc_paid":null,"fwci":1.7488,"has_fulltext":false,"cited_by_count":61,"citation_normalized_percentile":{"value":0.86109661,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"28","issue":"3","first_page":"549","last_page":"558"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.17170000076293945,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.17170000076293945,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12246","display_name":"Chronic Disease Management Strategies","score":0.09000000357627869,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10071","display_name":"Bone health and osteoporosis research","score":0.04960000142455101,"subfield":{"id":"https://openalex.org/subfields/2732","display_name":"Orthopedics and Sports Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.6690967082977295},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5424067378044128},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40639764070510864},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33688515424728394},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2862992286682129},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12813159823417664}],"concepts":[{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.6690967082977295},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5424067378044128},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40639764070510864},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33688515424728394},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2862992286682129},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12813159823417664}],"mesh":[{"descriptor_ui":"D000328","descriptor_name":"Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000328","descriptor_name":"Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000328","descriptor_name":"Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000368","descriptor_name":"Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008297","descriptor_name":"Male","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008875","descriptor_name":"Middle Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008875","descriptor_name":"Middle Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008875","descriptor_name":"Middle Aged","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011379","descriptor_name":"Prognosis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011379","descriptor_name":"Prognosis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011379","descriptor_name":"Prognosis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D015982","descriptor_name":"Bias","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015982","descriptor_name":"Bias","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015982","descriptor_name":"Bias","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015999","descriptor_name":"Multivariate Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015999","descriptor_name":"Multivariate Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015999","descriptor_name":"Multivariate Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016016","descriptor_name":"Proportional Hazards Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016016","descriptor_name":"Proportional Hazards Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016016","descriptor_name":"Proportional Hazards Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D018570","descriptor_name":"Risk Assessment","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D018570","descriptor_name":"Risk Assessment","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D018570","descriptor_name":"Risk Assessment","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1093/jamia/ocaa283","is_oa":false,"landing_page_url":"https://doi.org/10.1093/jamia/ocaa283","pdf_url":null,"source":{"id":"https://openalex.org/S129839026","display_name":"Journal of the American Medical Informatics Association","issn_l":"1067-5027","issn":["1067-5027","1527-974X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of the American Medical Informatics Association","raw_type":"journal-article"},{"id":"pmid:33236066","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33236066","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of the American Medical Informatics Association : JAMIA","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:7936516","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7936516","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"J Am Med Inform Assoc","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:7936516","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7936516","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"J Am Med Inform Assoc","raw_type":"Text"},"sustainable_development_goals":[{"display_name":"No poverty","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322252","display_name":"Israel Science Foundation","ror":"https://ror.org/04sazxf24"},{"id":"https://openalex.org/F4320338335","display_name":"H2020 European Research Council","ror":"https://ror.org/0472cxd90"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1918881530","https://openalex.org/W1992459498","https://openalex.org/W1994347885","https://openalex.org/W2015185375","https://openalex.org/W2025242288","https://openalex.org/W2031564429","https://openalex.org/W2033649338","https://openalex.org/W2053339749","https://openalex.org/W2080238057","https://openalex.org/W2096907419","https://openalex.org/W2106665921","https://openalex.org/W2110768118","https://openalex.org/W2115098571","https://openalex.org/W2132996843","https://openalex.org/W2148092884","https://openalex.org/W2156097706","https://openalex.org/W2163749674","https://openalex.org/W2165884492","https://openalex.org/W2168536722","https://openalex.org/W2297716127","https://openalex.org/W2530395818","https://openalex.org/W2530920598","https://openalex.org/W2575763985","https://openalex.org/W2762658547","https://openalex.org/W2807251972","https://openalex.org/W2883147591","https://openalex.org/W2888109941","https://openalex.org/W2902802452","https://openalex.org/W2964031043","https://openalex.org/W2964256806","https://openalex.org/W2969931180","https://openalex.org/W2981869278","https://openalex.org/W2989570674","https://openalex.org/W3025479223","https://openalex.org/W3035885149","https://openalex.org/W4213286494","https://openalex.org/W4246556188","https://openalex.org/W4288282543","https://openalex.org/W6639920281","https://openalex.org/W6670681603","https://openalex.org/W6728551298","https://openalex.org/W6751917733","https://openalex.org/W6765645076"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"OBJECTIVE:":[0],"To":[1],"illustrate":[2],"the":[3,15,32,38,44,51,59,73,78,93,104,112,125,131,140,143,155,170,186,190,211,242],"problem":[4],"of":[5,14,34,61,111,124,142,189,210,237,244],"subpopulation":[6,133,226,245],"miscalibration,":[7],"to":[8,18],"adapt":[9],"an":[10,207],"algorithm":[11,75,213,234],"for":[12,180,198,235],"recalibration":[13,74,236],"predictions,":[16],"and":[17,43,54,67,76,96,152,157,174,195,220,231,247,251],"validate":[19],"its":[20,222],"performance.":[21],"MATERIALS":[22],"AND":[23],"METHODS:":[24],"In":[25,201],"this":[26,202],"retrospective":[27],"cohort":[28],"study,":[29],"we":[30,204],"evaluated":[31,206],"calibration":[33,81,110,119,134],"predictions":[35],"based":[36],"on":[37],"Pooled":[39],"Cohort":[40],"Equations":[41],"(PCE)":[42],"fracture":[45],"risk":[46],"assessment":[47],"tool":[48],"(FRAX)":[49],"in":[50,55,80,92,103,120,154,164,185,193,224],"overall":[52,108],"population":[53],"subpopulations":[56,126,148,181],"defined":[57],"by":[58,150,215],"intersection":[60],"age,":[62],"sex,":[63],"ethnicity,":[64],"socioeconomic":[65],"status,":[66],"immigration":[68],"history.":[69],"We":[70],"next":[71],"applied":[72],"assessed":[77],"change":[79],"metrics,":[82],"including":[83],"calibration-in-the-large.":[84],"RESULTS:":[85],"1":[86,97],"021":[87],"041":[88],"patients":[89,100],"were":[90,101,136,183],"included":[91,102],"PCE":[94,156],"population,":[95],"116":[98],"324":[99],"FRAX":[105,158],"population.":[106],"Baseline":[107],"model":[109],"2":[113],"tested":[114],"models":[115,163,239],"was":[116,127],"good,":[117],"but":[118],"a":[121],"substantial":[122],"portion":[123],"poor.":[128],"After":[129],"applying":[130],"algorithm,":[132],"statistics":[135],"greatly":[137,240],"improved,":[138],"with":[139],"variance":[141],"calibration-in-the-large":[144],"values":[145],"across":[146],"all":[147],"reduced":[149,196],"98.8%":[151],"94.3%":[153],"models,":[159,191],"respectively.":[160],"DISCUSSION:":[161],"Prediction":[162],"medicine":[165],"are":[166],"increasingly":[167],"common.":[168],"Calibration,":[169],"agreement":[171],"between":[172],"predicted":[173],"observed":[175],"risks,":[176],"is":[177],"commonly":[178],"poor":[179],"that":[182],"underrepresented":[184],"development":[187],"set":[188],"resulting":[192],"bias":[194,243],"performance":[197],"these":[199],"subpopulations.":[200],"work,":[203],"empirically":[205],"adapted":[208],"version":[209],"fairness":[212,233,250],"designed":[214],"Hebert-Johnson":[216],"et":[217],"al.":[218],"(2017)":[219],"demonstrated":[221],"use":[223],"improving":[225],"miscalibration.":[227],"CONCLUSION:":[228],"A":[229],"postprocessing":[230],"model-independent":[232],"predictive":[238],"decreases":[241],"miscalibration":[246],"thus":[248],"increases":[249],"equality.":[252]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":6}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
